/// <summary>
/// Gets the sum of the areas of the rectangular features in an integral image.
/// </summary>
///
public double GetSum(IntegralImage2 image, int x, int y)
{
double sum = 0.0;
if (!Tilted)
{
// Compute the sum for a standard feature
foreach (HaarRectangle rect in Rectangles)
{
sum += image.GetSum(x + rect.ScaledX, y + rect.ScaledY,
rect.ScaledWidth, rect.ScaledHeight) * rect.ScaledWeight;
}
}
else
{
// Compute the sum for a rotated feature
foreach (HaarRectangle rect in Rectangles)
{
sum += image.GetSumT(x + rect.ScaledX, y + rect.ScaledY,
rect.ScaledWidth, rect.ScaledHeight) * rect.ScaledWeight;
}
}
return(sum);
}
/// <summary>
/// Detects the presence of an object in a given window.
/// </summary>
///
public bool Compute(IntegralImage2 image, Rectangle rectangle)
{
int x = rectangle.X;
int y = rectangle.Y;
int w = rectangle.Width;
int h = rectangle.Height;
double mean = image.GetSum(x, y, w, h) * invArea;
double var = image.GetSum2(x, y, w, h) * invArea - (mean * mean);
double sdev = (var >= 0) ? Math.Sqrt(var) : 1;
// For each classification stage in the cascade
foreach (HaarCascadeStage stage in cascade.Stages)
{
// Check if the stage has rejected the image
if (stage.Classify(image, x, y, sdev) == false)
{
return(false); // The image has been rejected.
}
}
// If the object has gone all stages and has not
// been rejected, the object has been detected.
return(true); // The image has been detected.
}
public void lena_test()
{
string localPath = NUnit.Framework.TestContext.CurrentContext.TestDirectory;
#region doc_lena
// In this example, we will compute an integral image
// representation of Lena Söderberg's famous picture:
TestImages testImages = new TestImages(path: localPath);
Bitmap lena = testImages["lena.bmp"]; // get the image
// Create a new Integral Image (squared and tilted) from Lena's picture:
IntegralImage2 ii = IntegralImage2.FromBitmap(lena, computeTilted: true);
// Let's say we would like to get the summed area in the rectangular region
// delimited by pixel (34, 50) until pixels (60, 105). This is equivalent to
// the region under the rectangle (34, 50, 34+60, 50+105) = (34, 50, 94, 155):
long sum = ii.GetSum(34, 50, 94, 155); // this is the sum of values (1760032)
// Now let's say we would like to get the squared sum and tilted sum as well:
long ssum = ii.GetSum2(34, 50, 94, 155); // this is the sum of squared values (229508896)
long tsum = ii.GetSumT(34, 50, 94, 155); // this is the sum of tilted values (-593600)
#endregion
Assert.AreEqual(1760032, sum);
Assert.AreEqual(229508896, ssum);
Assert.AreEqual(-593600, tsum);
}
public void GetSumTest()
{
byte[,] img =
{
{ 01, 02, 03, 04, 05, 06, 07, 08, 09, 10 },
{ 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 },
{ 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 },
{ 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 },
{ 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 },
{ 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 },
{ 61, 62, 63, 64, 65, 66, 67, 68, 69, 70 },
{ 71, 72, 73, 74, 75, 76, 77, 78, 79, 80 },
{ 81, 82, 83, 84, 85, 86, 87, 88, 89, 90 },
{ 91, 92, 93, 94, 95, 96, 97, 98, 99, 100 }
};
// Create integral image
Bitmap bmp = Accord.Imaging.Tools.ToBitmap(img);
IntegralImage2 ii = IntegralImage2.FromBitmap(bmp, 0, true);
// Horizontal rectangular feature
int x = 6, y = 0, w = 3, h = 2;
int expected = 7 + 8 + 9 + 17 + 18 + 19;
long actual = ii.GetSum(x, y, w, h);
Assert.AreEqual(expected, actual);
}
/// <summary>
/// Gets the sum of the areas of the rectangular features in an integral image.
/// </summary>
///
public double GetSum(IntegralImage2 image, int x, int y)
{
double sum = 0.0;
if (!Tilted)
{
// Compute the sum for a standard feature
foreach (HaarRectangle rect in Rectangles)
{
sum += image.GetSum(x + rect.ScaledX, y + rect.ScaledY,
rect.ScaledWidth, rect.ScaledHeight) * rect.ScaledWeight;
}
}
else
{
// Compute the sum for a rotated feature
foreach (HaarRectangle rect in Rectangles)
{
sum += image.GetSumT(x + rect.ScaledX, y + rect.ScaledY,
rect.ScaledWidth, rect.ScaledHeight) * rect.ScaledWeight;
}
}
return sum;
}